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Unplanned pregnancy affects up to 65% of women in some regions of Brazil, contributing to unsafe abortions and maternal mortality. The copper IUD is an effective long-term contraceptive but is underused, with only 4.4% of women of reproductive age using it. One barrier is the pain during insertion, leading to low adherence. Photobiomodulation (PBM), which has anti-inflammatory and analgesic effects, may offer a solution. This study aims to assess PBM's efficacy as a preemptive analgesic during copper IUD insertion in a randomized, double-blind trial involving 72 participants. The experimental group (n=36) will receive active PBM, while the control group (n=36) will receive PBM simulation. Pain will be measured using the Visual Analog Scale (VAS) at multiple time points, and additional outcomes include analgesic use, quality of life (WHOQOL-100), anxiety (GAD-7), satisfaction, and adverse effects. Statistical analysis will include tests such as the Friedman test, logistic regression, and ANOVA, with a significance level of 5%.
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Unplanned pregnancy affects up to 65% of women in some regions of Brazil, increasing the risks of unsafe abortions and contributing to maternal mortality. The copper IUD is an effective and long-lasting contraceptive option, but its use is still limited in Brazil, covering only 4.4% of women of reproductive age. One of the main barriers is the pain associated with its insertion, which leads to fear and low adherence to the method. Since pain can be of visceral or somatic origin, traditional approaches such as anti-inflammatories and anesthetics have shown inconclusive results in reducing this discomfort. Photobiomodulation (PBM) exhibits anti-inflammatory and analgesic effects and has demonstrated positive results in managing pelvic pain in various clinical contexts, including labor and delivery. The objective of this study is to evaluate the efficacy of PBM as a preemptive analgesic method during the insertion of the T 380 copper IUD. A randomized, double-blind clinical trial will be conducted with 72 participants randomly allocated into an experimental group (n=36) - active PBM and a control group (n=36) - PBM simulation. Patients will follow the IUD insertion protocol as outlined in the Ministry of Health guidelines. Pain will be assessed at different time points using the Visual Analog Scale (VAS) during the insertion phases (Pozzi, hysterometry, and IUD insertion), at 5 and 15 minutes, and at 24 and 48 hours after IUD insertion.
Additionally, analgesic use and quality of life (as measured by the WHOQOL-100) will be assessed over 48 hours, along with anxiety levels (as measured by the GAD-7), satisfaction with the procedure immediately after insertion (at 15 minutes), and adverse and side effects within 48 hours. The duration of pain, in hours, from the moment of IUD insertion until its resolution, will also be evaluated, as well as the success rate of the procedure. Statistical analysis will be performed using SPSS software version 24.0, with a significance level of 5% (p < 0.05). Data normality will be assessed using the Shapiro-Wilk test. Student's t-test or the Mann-Whitney test will be used for continuous variables, while the chi-square test or Fisher's exact test will be applied for categorical variables. Pain will be analyzed using the Friedman test, and logistic regression will be used to evaluate associations between groups and adverse effects. Statistical analysis will be performed with a significance level of 5%. Data normality will be assessed using the Shapiro-Wilk test. For pain analysis (VAS) and variables such as anxiety and quality of life, the Friedman test will be applied. Analgesic use will be evaluated using a repeated measures analysis of variance (ANOVA). Adverse effects will be analyzed using logistic regression. The time to resolve abdominal discomfort will be estimated using Kaplan-Meier analysis, and IUD insertion success will be compared using the chi-square test.
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72 participants in 2 patient groups
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Anna Carolina Ratto Tempestini Horliana, PhD; ANNA CAROLINA N FERRAZ, PhD student
Data sourced from clinicaltrials.gov
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